Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework
arXiv:2002.10671
Abstract
Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging. Recently, federated learning is proposed to train a globally shared model by exploiting a massive amount of user-generated data samples on IoT devices while preventing data leakage. However, the device, statistical and model heterogeneities inherent in the complex IoT environments pose great challenges to traditional federated learning, making it unsuitable to be directly deployed. In this article we advocate a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, we investigate emerging personalized federated learning methods which are able to mitigate the negative effects caused by heterogeneity in different aspects. With the power of edge computing, the requirements for fast-processing capacity and low latency in intelligent IoT applications can also be achieved. We finally provide a case study of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications.
arXiv commentSubmitted for review
References in corpus (11)
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- Federated Learning with Non-IID Data
- Federated Learning with Personalization Layers
- FedMD: Heterogenous Federated Learning via Model Distillation
- Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
- Improving Federated Learning Personalization via Model Agnostic Meta Learning
- Variational Federated Multi-Task Learning
- Federated Learning for Healthcare Informatics
- Personalized and Private Peer-to-Peer Machine Learning
- FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
- HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning